Reducing Energy Consumption and Material Loss in Steel Production Using Predictive Machine Learning

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In 2020, global steel production totaled 1,860 million tonnes, leading to the emission of over 3 billion tonnes of CO2, roughly 8% of all man-made greenhouse gas emissions. Steel producers are facing increasing pressure to reduce the CO2 emissions associated with steel production, both from governments and from industrial consumers. One aspect of this is to apply machine learning (ML) methods to analyse sensor data in steel plants, and optimise the process parameters. A steelworks can have hundreds of thousands of sensors, providing information related to some 5,000+ process parameters. However, operational decisions are generally based on the skills and experience of the operators. There is an opportunity to employ data-driven approaches to optimise these processes, increasing productivity, reducing energy consumption, and reducing wasted material. This project is a collaboration between four partners:* Deep.Meta is a start-up founded in 2021\. We are a team of ML engineers, software developers and metallurgists. We focus on the use of ML algorithms to generate real-time insights enabling operators to update control parameters to improve the performance of key processes in steelmaking.* MPI is the UK's leading research institute for metals R&D. MPI will lead work on electric arc furnace (EAF) operation, carrying out 32 melts using their pilot-scale plant, enabling Deep.Meta to trial optimisation strategies and measure the results.* Spartan UK is an industrial scale steelworks based in Gateshead. Spartan operates a hot-rolling mill, which is red by four reheat furnaces, supplying steel slabs at 1250ºC for subsequently rolling to form structural steels for construction products for buildings and bridges, as well as "yellow goods" manufactured by JCB, Caterpillar and Komatsu. Deep.Meta will work with Spartan to analyse process data and create algorithmic schedules for the reheat furnaces and rolling mill that optimise throughput, and prevent material loss due to oxidation (which occurs if the steel slabs spend too long at elevated temperatures in the furnace).* Grosvenor is a major UK property developer. Sustainable buildings are becoming a key differentiator, especially for high-end tenants. Reducing "embodied carbon" emitted to create the construction materials adds significant value. Grosvenor will lead a life-cycle assessment to determine the impact of the energy savings and resource efficiency improvements in the context of the construction industry, helping them to evidence and quantify the impact of choosing more sustainably produced steels.